Learning data generation apparatus and learning data generation method

ABSTRACT

A learning data generation apparatus includes a memory and a processor configured to perform determination of a region of interest in each of a plurality of images related to a learning target for machine learning in accordance with a result of image matching between the plurality of images, apply an obscuring processing to a specific region other than the region of interest in each of the plurality of images, and generate learning data including the plurality of images to which the obscuring processing is applied.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2018-223405, filed on Nov. 29,2018, the entire contents of which are incorporated herein by reference.

FIELD

The embodiment discussed herein is related to a technique of generatinglearning data.

BACKGROUND

In the field of artificial intelligence for automatically classifyingimages on each of which various objects are photographed, efforts havebeen recently made to enhance the correct ratio of classification. Forexample, a convolutional neural network (CNN) has been known as a modelof artificial intelligence.

In classification processing using the CNN, a convolution operation isperformed on an input image to extract features from the image, and theimage is classified into any of classes based on the extracted features.In this case, by changing the number of layers in deep learning or thenetwork structure, it is possible to improve the feature extractionaccuracy and thereby enhance the correct ratio of the classification.Various features are known as features extracted from an image.

There have been also known an image retrieval method of retrieving animage similar to a query image, a feature point selection system ofselecting feature points from a three-dimensional shape model, and animage processing apparatus of associating the three-dimensionalcoordinates of an observation target with the two-dimensionalcoordinates of a camera image.

For example, related arts are disclosed in Japanese Laid-open PatentPublication Nos. 2011-008507, 2010-218051, and 2014-038566; A.Krizhevsky et al., “Image Net Classification with Deep ConvolutionalNeural Networks”, NIPS′12 Proceedings of the 25th InternationalConference on Neural Information Processing Systems, Volume 1, Pages1097-1105, December 2012; E. Rublee et al., “ORB: an efficientalternative to SIFT or SURF”, ICCV′11 Proceedings of the 2011International Conference on Computer Vision, Pages 2564-2571, November2011; P. F. Alcantarilla et al., “KAZE Features”, Computer Vision-ECCV2012, Pages 214-227, 2012; D. G. Lowe, “Distinctive Image Features fromScale-Invariant Keypoints”, International Journal of Computer Vision,Volume 60 Issue 2, Pages 91-110, November 2004; and H. Bay et al.,“Speeded-Up Robust Features (SURF)”, Computer Vision and ImageUnderstanding, Volume 110 Issue 3, Pages 346-359, June 2008.

SUMMARY

According to an aspect of the embodiments, a learning data generationapparatus includes a memory and a processor configured to performdetermination of a region of interest in each of a plurality of imagesrelated to a learning target for machine learning in accordance with aresult of image matching between the plurality of images, apply anobscuring process to a specific region other than the region of interestin each of the plurality of images, and generate learning data includingthe plurality of images to which the obscuring process is applied.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a functional configuration diagram of a learning datageneration apparatus;

FIG. 2 is a flowchart of earning data generation processing;

FIG. 3 is a functional configuration diagram of an image classificationsystem;

FIG. 4 is a diagram illustrating an image;

FIGS. 5A and 58 are diagrams illustrating an image in a rectangularregion and a frequency distribution therein;

FIG. 6 is a diagram illustrating a relationship between a frequencyvalue and a filter region;

FIG. 7 is a diagram illustrating filter regions;

FIG. 8 is a diagram illustrating an obscured image;

FIG. 9 is a flowchart illustrating a specific example of the learningdata generation processing;

FIG. 10 is a flowchart of image classification processing;

FIGS. 11A and 11B are diagrams illustrating classification results; and

FIG. 12 is a diagram Illustrating a configuration of an informationprocessing apparatus.

DESCRIPTION OF EMBODIMENTS

In order to enhance the correct ratio of classification of an image, itis effective to extract features leading to enhancement of theclassification accuracy, from objects photographed in the imageincluding a classification target object such as a person andnon-classification target objects such as a background. In the case ofimage classification using the CNN, however, the accuracy of extractingfeatures of a classification target object may decrease depending on thenumber of images given as learning data to the CNN. Note that thisproblem occurs not only in the image classification using the CNN butalso in image processing using another model based on machine learning.

Hereinafter, an embodiment will be described in detail with reference tothe accompanying drawings. In the case of classification using the CNN,if a huge number of images are given as learning data, the CNN makeslearning to extract only features of a classification target object fromthe images. The huge number of mages may be, for example, several tensof thousands of images or more.

Nevertheless, if only a small number of images are given as learningdata, learning to also extract features of a non-classification targetobject, which are unnecessary for the classification, may be highlypossibly made because a single image has large influence on the learningresult. The small number of images may be, for example, several tens toseveral hundreds of images.

To address this, a segmentation is performed to cut out a region where aclassification target object is photographed from an Image, so thatextraction of features of a non-classification target object may bereduced. In order to perform the segmentation, however, work forgenerating correct data for supervised learning is required.

FIG. 1 is a functional configuration diagram of a learning datageneration apparatus according to an embodiment. A learning datageneration apparatus 101 in FIG. 1 includes a storage unit 111, anidentification unit 112, an obscuration unit 113, and a generation unit114. The storage unit 111 stores multiple images related to a learningtarget object in machine learning. The identification unit 112, theobscuration unit 113, and the generation unit 114 perform learning datageneration processing by use of the multiple images stored in thestorage unit 111.

FIG. 2 is a flowchart illustrating an example of the learning datageneration processing performed by the learning data generationapparatus 101 in FIG. 1. First, the identification unit 112 identifies aregion of interest in each of the images stored in the storage unit 111,based on a result of image matching between the concerned image and therest of the images (step 201).

Next, the obscuration unit 113 applies an obscuring process to a regionother than the region of interest in each of the multiple images storedin the storage unit 111 (step 202). Thereafter, the generation unit 114generates learning data including the multiple images to which theobscuring process is applied (step 203).

The learning data generation apparatus 101 as described above enablesenhancement of the accuracy of identifying an object photographed in animage in image processing using machine learning.

FIG. 3 illustrates a functional configuration example of an imageclassification system including the learning data generation apparatus101 in FIG. 1. The image classification system in FIG. 3 includes thelearning data generation apparatus 101 and an image classificationapparatus 301.

The learning data generation apparatus 101 includes the storage unit111, the identification unit 112, the obscuration unit 113, thegeneration unit 114, and a communication unit 311. The storage unit 111stores multiple images 321 on each of which a classification targetobject of the same type as a learning target object in machine learningis photographed. For example, if the learning target object is a person,each image 321 is an image on which a person is photographed. If thelearning target object is an automobile, each image 321 is an image onwhich an automobile is photographed. If the learning target object is aship, each image 321 is an image on which a ship is photographed.

The type of a classification target object photographed in an image 321may be determined visually by a user, or be determined by the learningdata generation apparatus 101 based on a tag added to the image 321.

FIG. 4 illustrates an example of an image 321. In this example, thelearning target object is a ship, and a background as non-classificationtarget objects are photographed together with the ship as theclassification target object in the image in FIG. 4. For example,mountains are included in regions 402 and 403, buildings are included ina region 404, and the sea is included in a region 405.

Even if a rectangular region 401 on which the ship is photographed iscut out by using a boundary box in order to reduce the influence of thebackground, part of the background is included in the rectangular region401. For this reason, in the learning by the CNN, the CNN alsounavoidably learns information on non-classification target objectsphotographed around a classification target object when learninginformation on the classification target object.

To address this, the identification unit 112 performs image matchingbetween each of the images 321 and the rest of the images 321 to obtaina frequency distribution 322 of feature points in the image 321, andstores the obtained frequency distribution 322 into the storage unit111. After that, the identification unit 112 identifies a region ofinterest in each image 321 based on the frequency distribution 322.

Use of images 321 on each of which an object of the same type as thelearning target object is photographed makes it possible to performimage matching between the images, and associate the feature points inan image with the feature points in the other images. For example, theidentification unit 112 may perform the image matching by using featuresas listed below:

-   -   (F1) Oriented FAST and Rotated BRIEF (ORB) described in E.        Rublee et al.;    -   (F2) KAZE features described in P. F. Alcantarilla et al.;    -   (F3) Accelerated-KAZE features;    -   (F4) Scale-invariant feature transform (SIFT) features described        in D. G. Lowe; and    -   (F5) Speeded-up robust features (SURF) described in H. Bay et        al.

The frequency distribution 322 includes a frequency value for each offeature points in a processing target image 321, and the frequency valuefor each concerned feature point indicates the number of feature pointsin all the other images 321 associated with the concerned feature point.The identification unit 112 generates the frequency distribution 322 bymapping the feature points in all the other images 321 to the processingtarget image 321.

FIGS. 5A and 5B illustrate examples of an image in a rectangular regionon which the ship is photographed and the frequency distribution 322therein. FIG. 5A illustrates the example of the image in the rectangularregion and FIG. 5B illustrates the example of the frequency distribution322 in the rectangular region in FIG. 5A. In the frequency distribution322 in FIG. 5B, a color of a pixel corresponding to each of featurepoints extracted from the rectangular region in FIG. 5A is changedaccording to the frequency value for the concerned feature point torepresent the distribution of the frequency values in the rectangularregion.

For example, the identification unit 112 extracts, as a region ofinterest, a group of feature points each having a larger frequency valuethan a predetermined threshold in the frequency distribution 322, Thus,only the feature points representing the shape of the classificationtarget object in the rectangular region may be extracted as a region ofinterest.

The obscuration unit 113 obtains the frequency value of each of thefeature points included in the region other than the region of interestby referring to the frequency distribution 322 of each image 321, anddetermines a specific region including each of the feature pointsaccording to the frequency value of the feature point. The obscurationunit 113 applies an obscuring process to the determined specific region,and thereby obscures the image in the region other than the region ofinterest. For example, the obscuration unit 113 may applies any ofobscuring processes as listed below:

(P1) Blur process;

(P2) Contrast change process,

(P3) Grayscale process;

(P4) Sepia-tone process;

(P5) Dilating process;

(P6) Eroding process; and

(P7) Jitter process.

The blur process is a process of applying a filtering process with ablur filter to the pixels in the specific region to replace the pixelvalues of these pixels with a statistical value. For example, a filtersuch as Blur filter, Gaussian_blur, or median_blur may be used as theblur filter, and a value such as an average value, a median value, amaximum value, or a minimum value of the pixel values within thespecific region may be used as the statistical value.

The contrast change process is a process of increasing or decreasing thebrightness, hue, luminance, saturation, or the like of the pixels withinthe specific region to reduce the information amount. The grayscaleprocess and the sepia-tone process are processes of narrowing adispersion of RGB values of the pixels within the specific region toreduce the information amount.

The dilating process is a process of replacing the brightness value of afeature point with the maximum brightness value within the specificregion including the feature point, whereas the eroding process is aprocess of replacing the brightness value of a feature point with theminimum brightness value within the specific region including thefeature point. By the dilating process or the eroding process, it ispossible to reduce the information amount of the feature point accordingto the conditions around the feature point. The jitter process is aprocess of randomizing the pixel values of the pixels within thespecific region to convert them into noise, so that features are lesslikely to be extracted.

The obscuration unit 113 may also apply a different type of obscuringprocess depending on a non-classification target object photographed inthe image 321. For example, if the background includes a sea surface,the features representing light reflection by the sea surface are madeless likely to be extracted by application of a process of reducing thecontrast. If the background includes waves, the features representingthe waves are made less likely to be extracted by application of thedilating process or the eroding process. If the background includes anobject such as a mountain or a building, the contour line of the objectis converted into noise by application of the jitter process, and thefeatures representing the object are less likely to be extracted.

The type of obscuring process applied to each image 321 may bedesignated by a user. Alternatively, the obscuration unit 113 mayidentify a non-classification target object by using a predeterminedalgorithm and determine the type of obscuring process depending on theidentified non-classification target object.

FIG. 6 illustrates an example of a relationship between a frequencyvalue for each feature point and a filter region in the case where theblur process is used as the obscuring process. In FIG. 6, the horizontalaxis indicates the frequency value for a feature point and the verticalaxis indicates the area of a filter region which is a specific regionincluding the feature point. In this example, the area of the filterregion for a feature point having a frequency value equal to or smallerthan a threshold T decreases stepwise with an increase in the frequencyvalue. Note that, since a feature point having a larger frequency valuethan the threshold T is extracted as a region of interest, the filterregion is not set for the feature point.

When the filter region is narrowed with an increase in the frequencyvalue, an image region to be obscured may be more limited for a featurepoint that matches with those in the other images 321 at a higherfrequency. This makes it possible to increase the obscuration degree asthe possibility that the feature point represents the shape of theclassification target object becomes lower, and decrease the obscurationdegree as the possibility that the feature point represents the shape ofthe classification target object becomes higher.

FIG. 7 illustrates an example of filter regions in two sizes set for afeature point. When the frequency value for a feature point 701 is equalto or smaller than a predetermined value, a 5×5 filter region 703 aroundthe feature point 701 is set. Meanwhile, when the frequency value forthe feature point 701 is larger than the predetermined value, a 3×3filter region 702 around the feature point 701 is set.

FIG. 8 illustrates an example of an image obscured by applying the blurprocess to the region other than the region of interest within therectangular region in FIG. 5A. In this case, the filter region is setfor each of feature points other than the feature points representingthe shape of the ship as the classification target object, and thefilter process using a blur filter is applied to the filter region. As aresult, the background of the ship is obscured.

When the region of interest only including the feature pointsrepresenting the shape of the classification target object is identifiedand the image in the region other than the region of interest isobscured as described above, the influence of the features of thenon-classification target object may be reduced. Thus, in the learningprocessing using obscured images 321, the features of thenon-classification target object are less likely to be extracted becausethe non-classification target object is made inactive.

The generation unit 114 generates learning data 323 including themultiple images 321 to which the obscuring process is applied and storesthe learning data 323 into the storage unit 111, and the communicationunit 311 transmits the learning data 323 to the image classificationapparatus 301 via a communication network 302.

The image classification apparatus 301 includes a communication unit331, a CNN 332, and a storage unit 333. The communication unit 331receives the learning data 323 from the learning data generationapparatus 101, and stores the received learning data 323 into thestorage unit 333.

The CNN 332 is a classifier for classifying images, and classifies aninput image into any of multiple classes according to features of aclassification target object photographed in the image. First, the CNN332 determines a parameter for each layer in a neural network byexecuting the learning processing using the learning data 323.

Next, the CNN 332 executes classification processing using aclassification target image 341 as input data and thereby classifies theimage 341 into any of the classes. The CNN 332 generates aclassification result 342 indicating a class to which the image 341 isclassified and stores the classification result 342 into the storageunit 333.

According to the image classification system in FIG. 3, even when thelearning data 323 includes only a small number of images 321, the CNN332 may efficiently learn the features of the classification targetobject photographed in these images 321. As a result, it is possible toenhance the accuracy of determining the classification target objectphotographed in each image 341, and thereby enhance the correct ratio ofthe classification.

FIG. 9 is a flowchart illustrating a specific example of the learningdata generation processing performed by the learning data generationapparatus 101 in FIG. 3. In this example, the storage unit 111 storesimages X1 to Xn (n is an integer of 2 or more) as multiple images 321.

First, the identification unit 112 sets 1 as a control variable irepresenting a process target image Xi (i=1 to n), sets 1 as a controlvariable j representing a comparison target image Xj (j=1 to n), andcompares i and j (step 901). If i=j (YES at step 901), theidentification unit 112 increments j by 1 and iterates the processing atstep 901.

On the other hand, if i≠j (NO at step 901), the identification unit 112performs image matching between the image Xi and the image Xj (step902), and calculates a frequency distribution Yi in the image Xi (step903). The identification unit 112 increments j by 1 and iterates theprocessing at step 901.

When j reaches n, the identification unit 112 identifies a region ofinterest in the image Xi based on the frequency distribution Yi. Next,the obscuration unit 113 sets the specific region for each feature pointincluded in the region other than the region of interest in the imageXi, and applies the obscuring process to the specific region thus set(step 904), The learning data generation apparatus 101 increments i by1, sets j to 1, and iterates the processing at step 901.

When i reaches n, the generation unit 114 generates learning data 323including the images X1 to Xn to which the obscuring process is applied.

FIG. 10 is a flowchart illustrating an example of the imageclassification processing. First, the CNN 332 performs the learningprocessing using the learning data 323 (step 1001). Next, the CNN 332performs classification processing to classify each image 341 into anyof the classes, and generates the classification result 342 indicatingthe class to which the image 341 is classified (step 1002).

FIGS. 11A and 11B illustrate examples of classification results for theimages X1 to Xn. In this example, n is 528 and each of 528 images isclassified into any of classes 0 to 13.

FIG. 11A illustrates the example of the classification result in thecase where the obscuring process is not applied to the images X1 to Xn.In the table in FIG. 11A, 14 columns represent the classes to which theimages are classified by the CNN 332, and 14 rows represent the correctclasses for the images. In other words, the number at the k-th row andm-th column (k=0 to 13 and m=0 to 13) indicates the number of imagesclassified to the class m among the images whose correct class is aclass k.

For example, there are 35 images whose correct class is the class 0, 20images among the 35 images are classified into the class 0, and theother 15 images are classified into the other classes. As a result, thecorrect ratio of the images whose correct class is the class 0 is 20/35.There are 40 images whose correct class is the class 1, 23 images amongthe 40 images are classified into the class 1, and the other 17 imagesare classified into the other classes. As a result, the correct ratio ofthe images whose correct class is the class 1 is 23/40. The totalcorrect ratio of all the 528 images is 437/528=82.7652%.

FIG. 11B illustrates the example of the classification result in thecase where the blur process is applied to the 75 images whose correctclasses are the classes 0 and 1. In this case, the correct ratio of theimages whose correct class is the class 0 is 26/35, and the correctratio of the images whose correct class is the class 1 is 33/40. Thetotal correct ratio of all the 528 images is 453/528=85.7955%. Thus, itis understood that applying the blur process to some of the imagesresults in enhancement of the correct ratio. The correct ratio isfurther enhanced when the blur process is applied to all the images.

The configuration of the learning data generation apparatus 101illustrated in FIG. 1 is merely an example, and part of the constituentelements of the learning data generation apparatus 101 may be omitted ormodified in accordance with usage or conditions of the learning datageneration apparatus 101.

The configuration of the image classification system in FIG. 3 is merelyan example, and part of the constituent elements of the imageclassification system may be omitted or modified in accordance withusage or conditions of the learning data generation apparatus 101. Forexample, if the CNN 332 is provided in the learning data generationapparatus 101, the communication unit 311 and the image classificationapparatus 301 may be omitted.

Instead of the CNN 332, another neural network such as a feedforwardneural network or a recurrent neural network may be used, or anothermodel based on machine learning may be used. For example, a model suchas a decision tree model, an association rule model, a geneticprogramming model, or a clustering model may be used instead of the CNN332.

The flowcharts in FIGS. 2, 9, and 10 are merely examples, and part ofthe processing may be omitted or modified in accordance with theconfiguration or conditions of the learning data generation apparatus101.

The images 321 illustrated in FIGS. 4 and 5A are merely examples, andthe image 321 is changed depending on a learning target object. Thefrequency distribution 322 illustrated in FIG. 5B is merely an example,and the frequency distribution 322 is changed depending on the images321.

The filter regions illustrated in FIGS. 6 and 7 are merely examples, anda filter region in a different size with a different shape may be used.The image illustrated in FIG. 8 is merely an example, and the obscuredimage is changed depending on a type of obscuring process. Theclassification results illustrated in FIGS. 11A and 11B are merelyexamples, and the classification result is changed depending on aclassification target image and a type of obscuring process.

FIG. 12 illustrates a configuration example of an information processingapparatus (computer) for use as the learning data generation apparatus101 in FIGS. 1 and 3 and the image classification apparatus 301 in FIG.3. The information processing apparatus in FIG. 12 includes a centralprocessing unit (CPU) 1201, a memory 1202, an input device 1203, anoutput device 1204, an auxiliary storage device 1205, a medium drivingdevice 1206, and a network coupling device 1207. These constituentelements are coupled to each other via a bus 1208.

The memory 1202 is, for example, a semiconductor memory such as aread-only memory (ROM), a random-access memory (RAM), or a flash memory,and stores a program and data to be used for processing. The memory 1202may be used as the storage unit 111 in FIGS. 1 and 3 or the storage unit333 in FIG. 3.

The CPU 1201 (processor) operates as the identification unit 112, theobscuration unit 113, and the generation unit 114 in FIGS. 1 and 3, forexample, by executing the program using the memory 1202. The CPU 1201also operates as the CNN 332 in FIG. 3 by executing the program usingthe memory 1202.

The input device 1203 is, for example, a keyboard, a pointing device, orthe like and is used for input of instructions or information from anoperator or a user. The output device 1204 is, for example, a displaydevice, a printer, a speaker, or the like, and is used for output ofinquiries or instructions to the operator or the user and output ofprocessing results.

The auxiliary storage device 1205 is, for example, a magnetic diskdrive, an optical disk drive, a magneto-optical disk drive, a tapedrive, or the like. The auxiliary storage device 1205 may be a hard diskdrive or a flash memory. The information processing apparatus stores theprogram and data in the auxiliary storage device 1205 and may use theprogram and data by loading them into the memory 1202. The auxiliarystorage device 1205 may be used as the storage unit 111 in FIGS. 1 and 3and the storage unit 333 in FIG. 3.

The medium driving device 1206 drives a portable recording medium 1209and accesses data recorded therein. The portable recording medium 1209is a memory device, a flexible disk, an optical disk, a magneto opticaldisk, or the like. The portable recording medium 1209 may be a compactdisc read-only memory (CD-ROM), a digital versatile disc (DVD), aUniversal Serial Bus (USB) memory, or the like. The operator or the usermay store the program and data in the portable recording medium 1209,and use the program and data by loading them into the memory 1202.

A computer-readable recording medium in which the program and data to beused for the processing are stored as described above is a physical(non-transitory) recording medium like the memory 1202, the auxiliarystorage device 1205, or the portable recording medium 1209.

The network coupling device 1207 is a communication interface circuitwhich is coupled to the communication network 302 in FIG. 3, andperforms data conversion for communication. The information processingapparatus may receive programs and data from external devices via thenetwork coupling device 1207 and use the programs and data by loadingthem into the memory 1202, The network coupling device 1207 may be usedas the communication unit 311 or the communication unit 331 in FIG. 3.

Note that the information processing apparatus does not have to includeall the constituent elements in FIG. 12, and part of the constituentelements may be omitted depending on its usage or conditions. Forexample, in the case where the information processing apparatus does nothave to interact with the operator or the user, the input device 1203and the output device 1204 may be omitted. In the case where theportable recording medium 1209 or the communication network 302 is notused, the medium driving device 1206 or the network coupling device 1207may be omitted.

Although the disclosed embodiment and its advantages have been describedin detail, a person skilled in the art could make various changes,additions, and omissions without departing from the scope of the presentdisclosure clearly described in the claims.

All examples and conditional language provided herein are intended forthe pedagogical purposes of aiding the reader in understanding theinvention and the concepts contributed by the inventor to further theart, and are not to be construed as limitations to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although one or more embodiments of thepresent invention have been described in detail, it should be understoodthat the various changes, substitutions, and alterations could be madehereto without departing from the spirit and scope of the invention.

What is claimed is:
 1. A learning data generation apparatus comprising:a memory; and a processor coupled to the memory and the processorconfigured to: perform determination of a region of interest in each ofa plurality of images related to a learning target for machine learningin accordance with a result of image matching between the plurality ofimages, apply an obscuring processing to a specific region other thanthe region of interest in each of the plurality of images, and generatelearning data including the plurality of images to which the obscuringprocessing is applied.
 2. The learning data generation apparatusaccording to claim 1 wherein each of the plurality of images includes anobject of a same type as the learning target.
 3. The learning datageneration apparatus according to claim 1, wherein the determinationincludes: obtaining, based on the result of the image matching, afrequency distribution of feature points in each of the plurality ofimages, and determining the region of interest based on the obtainedfrequency distribution.
 4. The learning data generation apparatusaccording to claim 3, wherein the specific region is determined inaccordance with a frequency value of each feature point in anotherregion other than the region of interest in each of the plurality ofimages.
 5. The learning data generation apparatus according to claim 1,wherein the obscuring processing includes at least one of a blurprocessing, a contrast change processing, a grayscale processing, asepia-tone processing, a dilating processing, an eroding processing, ora jitter processing.
 6. A compute implemented learning data generationmethod comprising: determining a region of interest in each of aplurality of images related to a learning target for machine learning inaccordance with a result of image matching between the plurality ofimages; applying an obscuring processing to a specific region other thanthe region of interest in each of the plurality of images; andgenerating learning data including the plurality of images to which theobscuring processing is applied.
 7. The learning data generation methodaccording to claim wherein each of the plurality of ages includes anobject of a same type as the learning target.
 8. The learning datageneration method according to claim 6 wherein the determining includes:obtaining, based on the result of the image matching, a frequencydistribution of feature points in each of the plurality of images, anddetermining the region of interest based on the obtained frequencydistribution.
 9. The learning data generation method according to claim8, wherein the specific region is determined in accordance with afrequency value of each feature points in another region other than theregion of interest in each of the plurality of images.
 10. The learningdata generation method according to claim 6, wherein the obscuringprocessing includes at least one of a blur processing, a contrast changeprocessing, a grayscale processing, a sepia-tone processing, a dilatingprocessing, an eroding processing, or a jitter processing.
 11. Anon-transitory computer-readable medium storing a program executable byone or more computers, the program comprising: one or more instructionsfor determining a region of interest in each of a plurality of imagesrelated to a learning target for machine learning in accordance with aresult of image matching between the plurality of images; one or moreinstructions for applying an obscuring processing to a specific regionother than the region of interest in each of the plurality of images;and one or more instructions for generating learning data including theplurality of images to which the obscuring processing is applied.